🤖The Role of Edge Computing in Industrial IoT🏭: Real-Time Analytics and Decision-Making at the Edge!💥⚡💥

Ciao a tutti, I´m back!

Starting from this month, I decided to publish my Industrial IoT newsletter bi-weekly, mostly in order to have enough time to retrieve relevant, consistent, and - most important of all - state-of-the-art insights, and actual case studies to share with you guys, in addition to less time available.

Hope you'll understand! 🚀🚀🚀

Fabio

<< How Edge Computing enables faster data processing and analysis [@ the Edge] in Industrial IoT applications, empowering real-time insights and enabling quick data-driven decision-making.>>

🔹In our increasingly interconnected world, Industrial IoT has emerged as a game-changer, enabling organizations to optimize processes, enhance efficiency, and gain valuable insights from vast amounts of data.

🔹However, the sheer volume and velocity of data generated in industrial environments present challenges in terms of

► real-time analytics

► decision-making.

🔹This is where Edge Computing steps in, transforming the way we harness the power of Industrial IoT: This innovative approach empowers faster data processing and analysis at the edge, enabling real-time insights and quick, data-driven decision-making.

Edge Computing brings the computational power and analytics closer to the data source, minimizing latency and bandwidth requirements.

🔹By processing data at or near the edge of the network, organizations can overcome the limitations of cloud-centric architectures, enhancing responsiveness and efficiency.

🔹We will see how edge computing enables industrial applications to process data in real-time, unleashing the potential for immediate action and reducing dependence on cloud connectivity.

From predictive maintenance to autonomous operations, the possibilities are endless.

🔹Join me as I explore the key advantages of Edge Computing in Industrial IoT, including

► enhanced data security

► reduced network congestion

► improved scalability.

🔹Finally, I will also showcase industry case studies that demonstrate successful implementations of edge computing, highlighting the tangible benefits and outcomes achieved.

So, don't miss out on the opportunity to gain valuable insights into this transformative technology: Stay connected, stay informed, and unlock the full potential of Industrial IoT with me!

Well done ChatGPT, as always, thanks for your inputs / suggestions! 👏😉👏

INSIGHTS & PERSPECTIVES

<<7 Edge Computing Trends To Watch In 2023 And Beyond | Via Informa TechTarget | December 2022>>

As Edge Computing continues to evolve, organizations are trying to bring data closer to the edge. We identify the top trends they should look out for.

"The world is brimming with data. And as organizations seek to use that data to make split-second decisions, they need compute power that can keep up."

That's where Edge Computing comes in.

🔹Edge Computing is a distributed information technology architecture that puts data processing, analysis and even intelligence as close as possible to the endpoints that are both generating the data and using the subsequent insights gleaned from that data to make decisions.

🔹Computing at the edge is typically housed in purpose-built devices such as edge gateways. However, that edge computing power can be housed in various devices, including the endpoints themselves. A smartphone, which provides some data processing services even when offline, is a case in point.

🔹Organizations across the board are evolving the technologies that support and surround edge computing, as well as how they're using edge computing technologies. Here are some noteworthy developments in this space to watch in 2023.

1. Spending on edge technology will continue to soar

🔹Market value figures vary widely, but there's consensus among multiple research and analyst reports that spending on edge is going up.

🔹The edge computing market will grow at a compound annual growth rate (CAGR) of 21.6% between 2022 and 2028 to hit an estimated $132.11 million. That's according to a fall 2022 report from ReportLinker .

🔹In announcing the findings, ReportLinker credited multiple factors for the growth, noting that

"the deployment of strong internet infrastructures, such as 5G and fiber optic cables, in developed countries, coupled with the rolling out of advanced infrastructures enable the establishment of new use cases, such as Fixed Wireless Access, Massive IoT, and Critical IoT. Such advancements in internet technology are driving individuals and businesses to harness the best out of the existing opportunities and maximize their revenue generation streams."

Furthermore,

"businesses across the industries, such as automotive, agriculture, oil & gas, healthcare, and manufacturing, are aware of the importance of IoT, communications, and sensors, which encourages the integration of sensors into devices. Enterprises and service providers perceive IoT as a key enabler of digital transformation and improvements in operational efficiencies. Thus, Edge Computing plays a key role in IoT deployment across various industries, which drives the edge computing market growth."

🔹Other research firms also predict significant growth -- and put the value significantly higher.

Grand View Research, Inc  estimates the global edge computing market will expand at a CAGR of 38.9% from 2022 to 2030. Precedence Research has estimated the global edge computing market will reach $51.2 billion in 2023 and surpass $116.5 billion by 2030.The "2022 Global Edge Computing Market report" put future growth even higher, estimating the market will hit $90 billion by 2030, driven in part by more integration of AI capabilities at the edge.

🔹Researchers consider the edge market to include

► hardware -- edge nodes, gateways, sensors and routers

► endpoint devices, such as drones and robots

► software

► edge-managed platforms

► services.

2. Endpoint devices and the data they generate are skyrocketing

🔹Market and consumer data firm Statista estimated the number of IoT devices worldwide will jump from 15.1 billion in 2023 to 29.4 billion in 2030.

🔹Nearly every industry makes use of endpoint devices and the data they generate.

• The automotive industry is one of the heaviest users of both endpoint devices and Edge Computing. For example, autonomous vehicles analyze data from sensors in near real time along the edge to provide the next course of action to self-driving vehicles.
• Consumer-facing industries, such as retail, want to support more personalized customer experiences by gathering and analyzing endpoint data in real time.
• Healthcare also relies on edge computing capabilities to process data coming from the myriad IoT devices used in patient care.

🔹Other industries advancing their use of IoT and Edge Computing capabilities include the following:

• agriculture, which uses the technology to support more efficient and effective practices in the field;
• industrial, manufacturing, energy and utilities, which typically look to the technology to monitor equipment and support leaner operations;
• smart cities and smart buildings, which use the technology to drive efficiencies and smooth everyday interactions such as traffic flow; and
• transportation and logistics, which use endpoint data and edge computing to optimize routes and supply-chain decisions while minimizing costs, resources and the time it takes to move goods.

3. Edge becomes an increasingly attractive target to threat actors

🔹Threat actors have taken note of the growing number of IoT devices and Edge Computing use cases, increasingly seeing them as prime targets.

🔹The " AT&T Cybersecurity Insights Report: Securing the Edge" found that 74% of the security, IT and line-of-business leaders they surveyed said the likelihood of their organization being compromised is a 4 or 5, with 5 being very likely.

🔹Respondents also rated ransomware as their highest threat. Other threats, ranked from the highest level of concern after ransomware, are the following:

• attacks against user/endpoint devices;
• sniffing attacks against the radio access network;
• attacks against server/data at the network edge;
• sniffing attacks against endpoint (user) devices and components;
• attacks against associated cloud workloads;
• attacks against applications at the network edge;
• supply chain attacks;
• attacks against the 5G core network (telco);
• physical attacks against technical components such as IoT devices and abandoned assets;
• DDoS against RAN; and
• attacks against multi-access edge computing.

4. 5G is on the march

🔹Although Edge Computing helps reduce latency by putting compute resources close to the endpoints generating data, the speed of 5G combined with Edge Computing further reduces latency to support use cases where near-real-time processing is critical.

🔹Because 5G creates a bigger, faster pipe to carry data, it can deliver the ultra-low latency required for many applications, including the widespread deployment of autonomous vehicles, advanced healthcare services such as remote telesurgery and the metaverse.

As such, many are cheering the expansion of 5G networks.

🔹They're being rewarded, too: According to Omdia , an analyst and consultancy firm, there were a half-billion connections at the end of 2021; the firm predicted that the figure would reach 1.3 billion by the end of 2022.

🔹And Omdia said 5G connections are expected to nearly double in 2023, reaching 2 billion connections by year's end; the firm further predicted that connections will reach 4.8 billion by the end of 2026.

🔹Wireless industry trade association 5GAmericas commented on the recent 5G expansion, saying that in 2022 "global wireless 5G adoption has reached the rapid acceleration phase."

"We are now out of the opening stages of this generation of wireless cellular technology, as 5G is rapidly getting into the hands of consumers and businesses, who are finding innovative new ways to use mobile connectivity," said Chris Pearson , president of 5GAmericas , in a press release responding to the Omdia figures.

5. Telecommunication companies become major players

🔹Despite their 19th-century roots, telecommunications companies have an expanding role in the 21st century evolution of IoT ecosystems.

🔹Telcos recognize that their widespread infrastructure and expansive reach puts them physically close to nearly all potential customers. More importantly, they can use that proximity to offer edge computing equipment, services and supporting components such as Secure Access Service Edge, which bundles network and security-as-a service functions and delivers them as a single cloud service.

🔹The June 2022 " Ericsson Mobility Report" called out this fact, noting that providing edge computing capabilities "represents huge untapped growth potential for service providers."

🔹The report further states that communications service providers (CSPs) are well-positioned to provide such services at an attractive cost.

"With the rollout of 5G, CSP mobile networks present an attractive proposition for running demanding enterprise applications close to target customers. A cost analysis of deployment shows that the cost to CSPs to deliver edge compute resources to enterprise customers is nearly half of what it would cost for an enterprise to build its own on-premise infrastructure with similar performance, reliability and data security."

6. 6G is on the horizon

🔹Even as 5G continues to be rolled out and heralded for its low latency and high bandwidth, many are already working to bring 6G to the market.

🔹Short for sixth-generation wireless, "6G networks leverage higher frequencies and higher capacity than 5G and still deliver significantly lower latency."

🔹The coming 6G networks will eventually replace 5G connectivity just as 5G is replacing 4G, which displaced earlier generations.

🔹And just as 5G provided capabilities that boosted edge computing and supported new use cases involving edge computing, 6G will offer new possibilities.

🔹In a post about its August 2022 report on 6G deployment, "Unlocking the Value of 6G with Distributed Intelligence," ABI Research highlighted the role distributed computing will play in the emerging environment:

"As 5G's commercial rollout continues, the deployment of distributed computing has become progressively more important. Distributed computing, or 'edge-to-cloud' compute, is the use of disaggregated resources to perform compute operations. But in the 5G era, distributed computing has played a supportive role, while, as enterprises and service providers transition to 6G, distributed computing will be given a leading role."

🔹 ABI Research further noted that "a sound distributed computing and artificial intelligence (AI) strategy will underpin successful 6G commercial deployment and enterprise use case enablement."

7. Providers turn to space

🔹Although 5G expansion and the emergence of 6G networks are boosting edge computing capabilities, space could aid edge computing cases even more, according to  The Linux Foundation 's "2022 State of the Edge" report.

"Thanks to the emerging private space sector, the costs of both space launches and satellite hardware continue to fall, while constellations of satellites in Low Earth Orbit (LEO) promise to make satellite internet connectivity faster, cheaper, and more reliable. That connectivity may be an ideal option for otherwise inaccessible edge locations," the report stated.

It continued: "Large geographical areas in many countries still have no mobile data coverage. Even developed countries with strong coverage have poor network performance in rural areas, because of terrain as well as the distance from the cell mast. Satellite connectivity is a key technology for expanding coverage of wireless communications networks to more remote areas, including oceans (oil rigs, for example, or cruise ships, which nowadays are effectively floating data centers, with huge connectivity needs), for temporary installations for sporting and entertainment events, and for emergency services."

Source: https://bit.ly/41SeMM2

#industrial #iot #iiot #ai #aiot #massiveiot #criticaliot #edgecomputing #trends #realtime #realtimeanalytics #realtimeinsights #datadrivendecisionmaking #endpoints #iotdevices #ransomware #5g #5gnetworks #telco #csp #6g #distributedcomputing #edgetocloud #computing #space #leo #satellite #connectivity

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<<IoT Edge Computing – What It Is And How It Is Becoming More Intelligent | IoT Analytics | November 2020>>

🔹IoT edge computing resources are becoming increasingly intelligent

🔹There are 7 key characteristics that make modern edge computing more intelligent (including open architectures, data pre-processing, distributed applications)

🔹The intelligent industrial edge computing market is estimated to reach $30.8B by 2025, up from $11.6B in 2020

🔹IT/OT architectures are evolving quickly

🔹Organizations that manage physical assets can reap tremendous cost savings and unlock new opportunities by switching to modern, intelligent edge computing architectures

Why has the interest in “edge computing" become so widespread in recent years?  

🔹The primary reason why the edge has become so popular in recent years is because the “edge” as we know it is becoming increasingly intelligent. This “intelligent edge” opens up a whole new set of opportunities for software applications and disrupts some of today’s edge to cloud architectures on all 6 layers of the edge.

🔹The hype about edge computing is warranted because the replacement of "dumb" edge computing with intelligent one has major implications for companies in all sectors, from consumer electronics and machinery OEMs to manufacturing facilities and oil and gas wells.

🔹Benefits of switching from “dumb” to “intelligent” edge computing architectures include an increase in system flexibility, functionality, scalability and in many cases a dramatic reduction in costs; one of the companies that was analyzed for the edge computing research realized a 92% reduction in industrial automation costs by switching to intelligent edge hardware.

Where is the edge?

🔹LF Edge (an umbrella organization under The Linux Foundation ) publishes an annual “State of the Edge” report which provides a modern, comprehensive and vendor-neutral definition of the edge. While these broad definitions are certainly helpful, the fact is that the edge is often “in the eye of the beholder”.  

🔹For instance, a telecommunications (telco) provider may view the edge as the micro datacenter located at the base of a 5G cell tower (often referred to as “Mobile Edge Computing” or MEC), while a manufacturing end user may view the edge as the vision sensor at the end of the assembly line.

🔹The definitions are different because the goal / purpose of hosting workloads at the edge is different:

► the telco provider is trying to optimize data consumption (i.e. performance issues associated with consumers of the data)

► while the manufacturing end user is trying to optimize data generation (i.e. performance issues associated with transmitting and analyzing the data). 

🔹 IoT Analytics  defines edge computing as a term used to describe "intelligent computational resources located close to the source of data consumption or generation". “Close” is a relative term and is more of a continuum than a static place. It is measured by the physical distance of a compute resource from its data source.

The three types of edge

A. Thick edge

🔹The thick edge describes compute resources (typically located within a data center) which are equipped with components designed to handle compute intensive tasks / workloads (e.g., high-end CPUs, GPUs, FGPAs, etc.) such as data storage and analysis. There are two types of compute resources located at the “thick” edge, which is typically located 100m to ~40 km from the data source:  

1. Cell tower data centers, which are rack-based compute resources located at the base of cell towers 
2. On prem data centers, which are rack-based compute resources located at the same physical location as the sensors generating the data 

B. Thin edge

🔹Thin edge describes the intelligent controllers, networking equipment and computers that aggregate data from the sensors / devices generating data. “Thin edge” compute resources are typically equipped with middle-tier processors (e.g., Intel i-series, Atom, Arm M7+, etc.) and sometimes include AI components such as GPUs or ASICs. There are 3 types of compute resources located at the “thin” edge, which is typically located at 1m to 1km from the data source.”: 

1. Computers, which are generic compute resources located outside of the data center (e.g., industrial PCs, Panel PCs, etc.) 
2. Networking equipment, which are intelligent routers, switches, gateways and other communications hardware primarily used for connecting other types of compute resources. 
3. Controllers, which are intelligent PLCs, RTUs, DCS and other related hardware primarily used for controlling processes.

C. Micro edge

🔹Micro edge describes the intelligent sensors / devices that generate data. “Micro edge” devices are typically equipped with low-end processors (e.g., Arm Cortex M3) due to constraints related to costs and power consumption. Since compute resources located at the “micro edge” are the data generating devices themselves, the distance from the compute resource is essentially zero. One type of compute resource is found at the micro edge: 

1. Sensors / devices, which are physical pieces of hardware that generate data and / or actuate physical objects. They are located at the very farthest edge in any architecture. 

🔹Modern intelligent edge computing architectures are the driving force behind the move to more edge computing and the value-creating use cases associated with the edge.

7 characteristics of intelligent edge computing

1. Open architectures  

🔹Proprietary protocols and closed architectures have been commonplace in edge environments for decades. However, these have often proven to lead to high integration and switching costs as vendors lock-in their customers. Modern, intelligent edge computing resources deploy open architectures that leverage standardized protocols (e.g., OPC UA, MQTT) and semantic data structures (e.g., Sparkplug) that reduce integration costs and increase vendor interoperability. An example for open protocols is  ICONICS  IoTWorX, an edge application which supports open, vendor-neutral protocols such as OPC UA and MQTT, among others.

2. Data pre-processing and filtering

🔹Transmitting and storing data generated by legacy edge computing resources in the cloud can be very expensive and inefficient. Legacy architectures often rely on poll / response setups in which a remote server requests a value from the “dumb” edge computing resource on a time-interval, regardless of whether or not the value has changed.

🔹Intelligent edge computing resources can pre-process data at the edge and only send relevant information to the cloud, which reduces data transmission and storage costs.

🔹An example of data pre-processing and filtering is an intelligent edge computing device running an edge agent that pre-processes data at the edge before sending it to the cloud, thus reducing bandwidth costs (see Amazon Web Services (AWS) project example).

3. Edge analytics

🔹Most legacy edge computing resources have limited processing power and can only perform one specific task / function (e.g., sensors ingest data, controllers control processes, etc.). Intelligent edge computing resources typically have more powerful processing capabilities designed to analyze data at the edge. These edge analytics applications enable new use cases that rely on low-latency and high data throughput.

🔹 Octonion , for example, uses ARM-based intelligent sensors to create collaborative learning networks at the edge. The networks facilitate the sharing of knowledge between intelligent edge sensors and allow end users to build predictive maintenance solutions based on advanced anomaly detection algorithms. 

4. Distributed applications 

🔹The applications that run on legacy edge computing devices are often tightly coupled to the hardware on which they run. Intelligent edge computing resources de-couple applications from the underlying hardware and enable flexible architectures in which applications can move from one intelligent compute resource to another.

🔹This de-coupling enables applications to move both vertically (e.g., from the intelligent edge computing resource to the cloud) and horizontally (e.g., from one intelligent edge computing resource to another) as needed.

🔹There are 3 types of edge architectures in which edge applications are deployed: 

1. 100% edge architectures. These architectures do not include any off-premises compute resources (i.e. all compute resources are on-premises). 100% edge architectures are often used by organizations that do not send data to the cloud for security / privacy reasons (e.g., defense suppliers, pharmaceutical companies) and / or large organizations that have already invested heavily in on-premise computing infrastructure. 
2. Thick edge + cloud architectures. These architectures always include an on-prem data center + cloud compute resources and optionally include other edge compute resources. Thick edge + cloud architectures are often found in large organizations that have invested in on-prem data centers but leverage the cloud to aggregate and analyze data from multiple facilities. 
3. Thin / micro edge + cloud architectures. These architectures always include cloud compute resources connected to one or more smaller (i.e. not on-prem data centers) edge compute resources. Thin / micro edge architectures are often used to collect data from remote assets that are not part of existing plant network.

🔹Modern edge applications need to be architected so that they can run on any of the 3 edge architectures. Lightweight edge “agents” and containerized applications in general are two examples of modern edge applications which enable more flexibility when designing edge architectures.

5. Consolidated workloads

🔹Most “dumb” edge computing resources run proprietary applications on top of proprietary RTOSs (real-time operating system) which are installed directly on the compute resource itself. Intelligent edge computing resources are often equipped with hypervisors which abstract the operating system and application from the underlying hardware.

🔹This enables an intelligent edge computing resource to run multiple operating systems and applications on a single edge device. This leads to workload consolidation, which reduces the physical footprint of the compute resources required at the edge and can result in lower COGS (cost of goods sold) for device or equipment manufacturers that previously relied on multiple physical compute resources.

🔹The example below shows how a hypervisor is used to run multiple operating systems (Linux, Windows, RTOS) and containerized applications (Docker 1, Win Container) all within a single piece of hardware.

6. Scalable deployment / management 

🔹Legacy compute resources often use serial (often proprietary) communication protocols which are difficult to update and manage at scale.

🔹Intelligent edge computing resources are securely connected to local or wide area networks (LAN, WAN) and can thus be easily deployed and managed from a central location.

🔹Edge management platforms are increasingly being used to handle the administrative tasks associated with large scale deployments. An example of an edge management platform is  Siemens ’ Industrial Edge Management System, which is used for deploying and managing workloads on Siemens’ intelligent edge compute resources.

7. Secure connectivity 

🔹“Security by obscurity” is a common practice for securing legacy compute devices. These legacy devices often have proprietary communication protocols and serial networking interfaces, which do add a layer of “security by obscurity”; however, this type of security comes at a cost of much higher management and integration costs.

🔹Advancements in cybersecurity technology (e.g., hardware security modules [HSMs]) are making it easier and safer than ever to securely connect intelligent devices. Different levels of security can be provided throughout the product lifecycle depending on the specific needs of the application.

🔹 NXP Semiconductors ' end-to-end security solution, for example, begins at the device manufacturing level and spans all the to the deployment of applications on the connected edge devices.

The market for intelligent edge computing

🔹The focus of our latest report on industrial edge computing explores the intelligent industrial edge in much greater depth. The report focusses on edge computing at industrial sites such as manufacturing facilities, power plants, etc.

🔹According to our findings, intelligent industrial edge computing will make up an increasingly large share of the overall industrial automation market, growing from ~7% of the overall market in 2019 to ~16% by 2025.

"The total market for intelligent industrial edge computing (hardware, software, services) reached $11.6B in 2019 and is expected to increase to $30.8B by 2025."

Source: https://bit.ly/3pUKDhZ

#industrial #iot #iiot #ai #aiot #intelligent #edge #computing #edgecomputing #edgeanalytics #openarchitectures #datapreprocessing #filtering #distributedapplications #workloads #secure #connectivity #cybersecurity

CASE STUDIES

<<Predictive Maintenance For Connected Vehicles ► London's Diesel Electric Hybrid Buses | Via Luxoft | April 8, 2022>>

<< Luxoft and Vantage Power Enable Powertrain Innovation and Safety with AWS IoT Telemetry | Via Amazon Web Services (AWS) >>

Vantage Power accelerates time-to-market by 6 months reducing operational costs by over 80%

🔹 Vantage Power  wanted a vehicle telemetry system that would deeply integrate into the powertrain, vehicle control software, and other existing systems to collect rich operational data that could be used to tell a comprehensive story about how vehicles and parts were performing.

🔹They wanted to create a way to closely monitor their lithium-ion battery systems to help customers detect cell-level defects early and mitigate issues, potentially preventing the replacement of a lithium-ion battery that can cost over $50,000 per bus.

“Battery-powered, heavy-duty vehicles are increasingly common, but successfully deploying and maintaining them at scale remains a challenge,” says Alexander Schey , CEO of Vantage Power .

“Using real-world data from the in-service batteries, we’ve developed a model that detects a failure mode in the cloud months earlier than we can today. We’re excited about how this platform can be applied to battery technology in all sectors to bring about a more robust and agile way of managing electric heavy-duty vehicles.”

Connected Fleet

🔹 Vantage Power worked with  Luxoft , to create a telemetry platform called #VPVision, which brings the Amazon Web Services (AWS) cloud platform to each connected vehicle >>> Edge Computing.

🔹Built around an Internet of Things (IoT) architecture, VPVision leverages AWS’ IoT services, fully customizable to specific engines and fleets:

► to optimize processing of hundreds of thousands of data points per minute

► 1hz frequency over cellular connectivity

► to derive insights and predictive analytics models

that can then be distributed to the vehicles to take preventative actions in real time >>> Predictive Maintenance (PdM)

• The system monitors everything from vehicle speed to engine health and battery-pack-level diagnostics. It enables operators and OEMs to receive insight and reporting for over 6,000 data points from each vehicle.
• VPVision collects, processes, stores, and presents real-time vehicle data, automatically, via the cloud.
• It enables real-time geolocation visualization and two-way communication between fleet managers and drivers.
• When routine maintenance is required, real-time alerts keep downtime to a minimum.

“Together with Vantage Power and using AWS, Luxoft has developed a solution that can provide incredible insights and value using the data from connected vehicles. Within seconds, complex data is transformed into actionable information and shown on the platform.” - Sam Mantle , former MD of Digital Enterprise at Luxoft

Reducing Costs Through Remote Diagnostics and Edge Computing

Machine learning models built on AWS can be deployed to edge devices [Edge Computing] to reduce cloud transaction and compute costs

🔹 Vantage Power and Luxoft created a diagnostic model that analyses data on-vehicle [Edge Computing] and reports back <<ONLY!>> when there’s an issue that needs attention.

🔹This minimizes the administrative burden traditionally experienced in monitoring, managing, scheduling, parts provisioning, fixing, and commissioning vehicles back into service faster.

🔹For example, if a refrigerated truck unexpectedly stops cooling, costs can be as high as $40,000 if the cargo temperature exceeds a threshold and the stock is scrapped.

"Costly breakdowns can be avoided by deploying predictive fault analysis to the edge and emerging issues are prevented from impacting operations".

Results

🔹Limits the time vehicles are in maintenance shops, reducing operational costs by over 80%.

🔹Lowers overall costs by streamlining aftermarket support with centralized, real-time data.

🔹Identifies the ideal time and location for lithium-ion battery balancing, extending the operational life of a battery by around 10%.

🔹Reduces costs and admin through the application of Edge Computing and remote diagnostics which analyze data, on-vehicle, and report back issues that need attention.

🔹Thanks to scenario modelling, plus early cell-level fault detection and mitigation, customers can avoid battery replacements which could cost over $50,000 per bus.

🔹Complies with new emissions regulations.

Sources: https://go.aws/3WkxsTG [AWS] / https://bit.ly/3ItdRep [Luxoft]

#industrial #iot #iiot #ai #aiot #intelligent #edge #computing #edgecomputing #edgeanalytics #realtimeanalytics #predictiveanalytics #predictivemaintenance #geolocation #visualization #connectedvehicles #heavyduty #electricvehicles #hybrid #buses #powertrain #lithiumionbattery

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NAVANTIA | Shipbuilding 4.0 Powered by Edge Computing and 5G

Navantia, S.A., S.M.E , a leading company in the manufacture of high-tech ships, has found in Telefónica Tech the best travelling partner on the road to its digital transformation

Shipyards 4.0: three use cases of 5G Edge Computing applied to ship reparation and construction processes have been defined:

🔹5G and Edge Computing for remote assistance

🔹5G and Edge Computing for real time processing of 3D scanning

🔹5G and Augmented Reality for Shipbuilding

"The use of Edge Computing and 5G is one of the key technologies for the evolution towards the 4.0 shipyard, which will optimise the entire production process and, in the case of Ferrol, will make the shipyard a leader in the construction of state-of-the-art frigates" - Donato Martínez , Technology and Digital Transformation Director at Navantia, S.A., S.M.E

Source: https://bit.ly/3OoNfPh

#industrial #iot #iiot #ai #aiot #shipbuilding #shipyards #edge #computing #edgecomputing #edgeanalytics #realtimeanalytics #remoteassistance #3dscanning #augmentedreality #ar

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APM Terminals | 5G, Edge Computing and V2X connectivity for industrial security

APM Terminals, one of the largest operators in port, maritime and land terminal design worldwide, joins this list of Edge Computing case studies thanks to its pilot project with Telefónica Tech

🔹 APM Terminals developed a pilot project to improve the security of its Barcelona terminal, using a combination of 5G, Edge Computing and C-V2X technology, focusing on these two case studies:

► Geolocation and virtual positioning of fixed objects

► Geolocation of moving elements

"Our customers don’t want to deal with paper. Now, Edge Computing with Internet of Things sensors on equipment, that incorporate Computer Vision and AI can give customers what they’ve longed for, for some time – almost instant access to cargo data upon arrival as well as automated repairs or fixes." - Gavin Laybourne - VP & Global CTIO - APM Terminals at A.P. Moller - Maersk

🔹The software can then decide whether there’s an intervention needed, such as maintenance or repair, and that information is released to the customer.

🔹Cameras and data collection IoT devices will be installed throughout terminals, via private 5G networks, to monitor for anything, be it theft, lost cargo, or potentially unsafe conditions.

Source: https://bit.ly/3ItfOHQ

#industrial #iot #iiot #ai #aiot #cargo #terminal #edge #computing #edgecomputing #edgeanalytics #realtimeanalytics #5g #v2x #computervision #connectivity #industrialsecurity #portoperations #maritime #land #terminal #geolocation

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IE University : Innovative Experience in Education with Edge Computing and 5G

Edge Computing has also reached classrooms and the best example of this is the case study of IE University

🔹Together with Telefónica Tech and Nokia , IE University has developed an immersive experience at its Campus in Segovia, thanks to the application of 5G and Edge Computing.

🔹Thanks to Edge Computing ability to bring processing power as close as possible to the source of data generation, this technology is enabling new educational methodologies to become effective:

► These are immersive virtual lessons where students learn in streaming and from their own devices. In this use case, a third key element is added to 5G and Edge Computing: Virtual Reality (VR)

► During the project, a prototype application based on Virtual Reality technology was developed for the teaching and dissemination of the architecture of Segovia through online virtual seminars.

► Thanks to 5G technology and Edge Computing combined with Virtual Reality "...students can remotely connect to these seminars to interact with the teacher and the rest of the students, without having to be in the same physical classroom..."

🔹It is an online architecture seminar where students learn within the same scenario they are studying, streaming it from their own devices, as if they were physically there. They do this with the help of 3 key deployed components:

• 5G: provides low latency to generate an easier and more comfortable immersive experience, a very important point in the VR environment so that some people don’t get dizzy.
• Edge Computing: deployed near the IE University campus, it offers minimal latency, generating closer content and faster processing of information, allowing the processing that would be done in a VR headset to be transferred to the Edge.
• VR: feedback from the Edge and 5G makes it possible to develop specific video technology to generate 3D video streams, allowing the processing that would be done in VR glasses to be transferred to the Edge, and improving the experience perceived by users.

“The possibilities offered by Telefónica's education technology are immense. In the future, the learning environment developed through 5G will offer students and teachers incredible immersive experiences that will surely revolutionise teaching processes; in fact, at the present time we have been working on it at IE University."- Miguel Larrañaga Zulueta - IE University Vice-Rector for Student Affairs and Professor Logo empre

Source: https://bit.ly/43idrj3 / https://bit.ly/3OsC2xm / https://bit.ly/3MoxiWF

#industrial #iot #iiot #ai #aiot #education #edge #computing #edgecomputing #edgeanalytics #5g #virtualreality #vr #onlineseminar #3dvideo #streams

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Source: https://bit.ly/3MKawu2

Bio: 🔹Fabio Bottacci is a relationship builder, creative problem solver, and strategic thinker. Senior industrial executive and former strategic consultant, he acquired a solid background in large multinational organizations across Brazil, US, and Western Europe. He is known for his ability to deliver results despite ambiguity and obstacles, to build bridges between people, and to manage conflict and negotiations.

Fabio graduated in Economics & Business Administration from Università Bocconi and, recently in 2020, from Stanford University School of Engineering in Internet of Things [IoT].

He began his career at Accenture Italia , strategy practice, while attending MBA courses. He then moved to Brazil, where he consistently proved, during more than 20 years of professional experience, strong clients' network, industry knowledge and business development expertise in the automotive, oil and gas, energy, and utilities verticals.

Since 2015, he has been the founder & CEO of VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀 , being recognized internationally as a thought leader by well-known organizations, such as the World Economic Forum , the IOT Solutions World Congress , and the BNDES (Brazilian Federal Development Bank).

Recently, Fabio has been contracted by the European Research Executive Agency (REA) as an Industrial IoT Expert / Evaluator for the HORIZON Europe Programme of the European Commission , and by Kobo Funds as Venture Partner, focused on DeepTech Seed/Series A startups, helping investors work successfully with entrepreneurs.

Fabio's mission is to help startups, SME, and corporations to thrive within the current digital transformation environment, by increasing productivity, developing new business models, and delivering actual results / ROI in months, not years!